Publication Type

Journal Article

Version

publishedVersion

Publication Date

3-2022

Abstract

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges.

Keywords

Anomaly detection, deep learning, outlier detection, novelty detection, one-class classification

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Intelligent Systems and Optimization

Publication

ACM Computing Surveys

Volume

54

Issue

2

First Page

1

Last Page

38

ISSN

0360-0300

Identifier

10.1145/3439950

Publisher

ACM

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3439950

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